Administrative Supplement to Support Collaborations to Improve AIML-Readiness of NIH-Supported Data for Parent Award SCH: Personalized Rescheduling of Adaptive Radiation Therapy for Head & Neck Cancer

项目摘要 我们在我们的父母奖(1R01CA257814-01)下收集了一个系列多参数数据库 磁共振(MR)图像以及患者报告的和客观的毒性措施 400名头颈部(HNC)患者，在治疗前、治疗中和治疗后。我们计划完整地利用这些数据 与家长奖的第一个具体目标保持一致，以有效量化与治疗相关的反应 在肿瘤/结节和正常组织上，以便制定个性化的治疗计划适应 个别HNC患者。然而，就我们所知，作为数据最丰富的图像毒性队列， 该数据库需要用于人工智能/机器学习(AI/ML)的严格管理 例如，预测肿瘤并发症概率(TCP)和正常组织并发症的方法 概率(Ntcp)。具体地说，肿瘤和正常组织感兴趣区域的多观察者分割 必填项。此外，传播工作是必要的，以便让人工智能/ML社区的专家参与 开发AI/ML-用于自动分段模型和TCP/NTCP预测的方法。为此，我们计划 实现三个具体目标。通过我们的第一个具体目标，我们计划策划我们的系列多参数， 多时间点MRI数据集(伴随着提取的放射组学)用于治疗反应和TCP 通过组建一个由三名医生组成的团队进行预测，以获得真实的分割图像。我们 进一步计划将经过精选的分割图像作为数据集存储到癌症成像档案馆(TCIA)。AS 我们的第二个具体目标是，我们计划对匹配的图像剂量多时间点进行管理和公开存放 急性和晚期毒性指标将分发给AI/ML专家用于NTCP建模。我们会 尤其包括患者报告的MD Anderson症状清单-头颈部(MDASI-HN)毒性 结果，共同毒性标准-不良事件(CTC-AE)医生分级的毒性，以及目标 吞咽功能障碍的测量方法，如改良的钡剂吞咽和管饲评估。在……里面 第三个具体目标，我们计划设计并执行一个针对连续图像剂量的公共众包挑战- 针对TCP和NTCP预测建模任务的响应预测。基于我们计划的测试数据集 为了在执行质询后发布，我们将对提交的 模型(例如，假阳性和假阴性案例)，并将最佳结果作为手稿传播 提交出版物和演示文稿。如果成功，拟议的努力将直接回应 需要支持AI/ML的数据集用于癌症治疗。

Project Summary We have collected, under our parent award (1R01CA257814-01), a database of serial multi-parametric magnetic resonance (MR) images as well as patient-reported and objective toxicity measures for more than 400 head and neck (HNC) patients, at pre-, on-, and post-therapy. We plan to utilize this data, in complete alignment with the first specific aim of the parent award, to effectively quantify treatment-related response on tumor/node and normal tissue in order to develop personalized treatment planning adaptations for individual HNC patients. As the most data-rich image toxicity cohort to the best of our knowledge, however, this database necessitates rigorous curation to be utilized for artificial intelligence/machine learning (AI/ML) approaches to predict, for example, tumor complication probability (TCP) and normal tissue complication probability (NTCP). Specifically, multi-observer segmentation of tumor and normal tissue regions of interest is required. Additionally, dissemination efforts are necessary to engage experts from AI/ML communities to develop AI/ML-approaches for auto-segmentation models, and TCP/NTCP predictions. To this end, we plan to undertake three specific aims. Through our first specific aim, we plan to curate our serial multi-parametric, multi time-point MRI dataset (accompanied with extracted radiomics) for therapeutic response and TCP prediction through assembling a team of three physicians to obtain the ground-truth segmented images. We further plan to deposit the curated segmented images as a dataset to The Cancer Imaging Archive (TCIA). As our second specific aim, we plan for curation and public deposition of matched image-dose multi-time-point acute and late toxicity metrics to be disseminated to both AI/ML experts for NTCP modeling. We will particularly include patient-reported MD Anderson Symptom Inventory-Head and Neck (MDASI-HN) toxicity outcomes, Common Toxicity Criteria- Adverse Events (CTC-AE) physician-ranked toxicity, and objective measures of swallowing dysfunction such as modified barium swallowing and tube-feeding assessments. In the third specific aim, we plan to design and execute a public crowdsourced challenge for serial image dose- response prediction for both TCP and NTCP prediction modeling tasks. Based on the test dataset that we plan to release after the execution of the challenge, we will conduct a post-challenge analysis on the submitted models (e.g., false-positive, and false-negative cases), and disseminate the best results as manuscripts to be submitted for publications and presentations. If successful, the proposed efforts are directly responsive to the need for AI/ML-ready datasets to be utilized for cancer treatment.